Motivated to resolve the mentioned limitations, we propose FED-CD - a general federated framework for causal structure discovery from observational and interventional data [1]}. We learn a global causal structure from a federation of learners (clients), without direct access to each learner's local data. To this end, clients in our federated setup adopt a neural causal discovery method, for instance [2]}, that relies on continuous optimization in finding a local model. The local models are then aggregated such that the following criteria are met. First, clients with more knowledge about intervened variables have larger impact on the discovery process. And second, no local samples need to be shared between the clients or to the server, only probabilistic updates to direct the search for a global structure.
